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Application of Chromatographic and Spectroscopic Techniques in Food Adulteration and...
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Application of Chromatographic and Spectroscopic Techniques in Food Adulteration and Traceability

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7238

Special Issue Editors

Dr. Sabrina Moret

E-Mail Website
Guest Editor
Department of Agri-Food, Animal and Environmental Sciences, University of Udine, Via Sondrio 2/a, 33100 Udine, Italy
Interests: food authentication; food fraud identification; food contaminants (biogenic amines, mycotoxins, polycyclic aromatic hydrocarbons, mineral oils, polyolefin oligomers, phthalates); food packaging contaminants; method validation; sample preparation; chromatographic techniques; online coupling
Special Issues, Collections and Topics in MDPI journals
Dr. Laura Barp

E-Mail Website
Guest Editor
Department of Agri-Food, Animal and Environmental Sciences, University of Udine, Via Sondrio 2/a, 33100 Udine, Italy
Interests: sample preparation; food contaminants; food compliance; food safety; food packaging contaminants; wine analysis; method development and validation; chromatographic techniques

Special Issue Information

Dear Colleagues,

Food authenticity and traceability are essential aspects of food quality and safety. In fact, tracing the origins and knowing the provenance of food products makes it possible not only to take corrective measures in case of contamination, but also to establish the authenticity of food, combat fraudulent practices and discourage adulteration. Adulteration is not only a significant economic problem but can also lead to serious health problems for consumers. As food adulteration methods have become more sophisticated, it has become necessary to develop increasingly efficient and reliable techniques to detect fraudulent manipulations. The main techniques that have been successfully applied to food authentication over the past 20 years are spectroscopy (UV, NIR, MIR, visible, Raman), isotopic analysis, chromatography, electron nose, polymerase chain reaction, enzyme-linked immunosorbent assay, thermal analysis and chemometric techniques.

This Special Issue focuses on food safety (e.g., food contaminants, food adulteration and traceability) and aims to exhibit advances in the field of food authentication, giving special emphasis to chromatographic and spectroscopic techniques.

Dr. Sabrina Moret
Dr. Laura Barp
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • adulteration
  • traceability
  • authenticity
  • food safety
  • foods
  • food contaminants
  • analytical methods
  • chromatography
  • spectroscopic techniques

Published Papers (5 papers)

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Research

16 pages, 966 KiB  
Article
Simultaneous Analysis of Organic Acids, Glycerol and Phenolic Acids in Wines Using Gas Chromatography-Mass Spectrometry
by Violeta Garcia-Viñola, Candela Ruiz-de-Villa, Jordi Gombau, Montse Poblet, Albert Bordons, Cristina Reguant and Nicolas Rozès
Foods 2024, 13(2), 186; https://doi.org/10.3390/foods13020186 - 05 Jan 2024
Viewed by 1070
Abstract
Fermented beverages, particularly wines, exhibit variable concentrations of organic and phenolic acids, posing challenges in their accurate determination. Traditionally, enzymatic methods or chromatographic analyses, mainly high-performance liquid chromatography (HPLC), have been employed to quantify these compounds individually in the grape must or wine. [...] Read more.
Fermented beverages, particularly wines, exhibit variable concentrations of organic and phenolic acids, posing challenges in their accurate determination. Traditionally, enzymatic methods or chromatographic analyses, mainly high-performance liquid chromatography (HPLC), have been employed to quantify these compounds individually in the grape must or wine. However, chromatographic analyses face limitations due to the high sugar content in the grape must. Meanwhile, phenolic acids, found in higher quantities in red wines than in white wines, are typically analyzed using HPLC. This study presents a novel method for the quantification of organic acids (OAs), glycerol, and phenolic acids in grape musts and wines. The approach involves liquid-liquid extraction with ethyl acetate, followed by sample derivatization and analysis using gas chromatography-mass spectrometry (GC-MS) in selected ion monitoring (SIM) detection mode. The results indicated successful detection and quantification of all analyzed compounds without the need for sample dilution. However, our results showed that the method of adding external standards was more suitable for quantifying wine compounds, owing to the matrix effect. Furthermore, this method is promising for quantifying other metabolites present in wines, depending on their extractability with ethyl acetate. Fermented beverages, particularly wines, exhibit variable concentrations of organic and phenolic acids, posing challenges in their accurate determination. Traditionally, enzymatic methods or chromatographic analyses, mainly high-performance liquid chromatography (HPLC), have been employed to quantify these compounds individually in the grape must or wine. The approach of this proposed method involves (i) methoximation of wine compounds in a basic medium, (ii) acidification with HCl, (iii) liquid-liquid extraction with ethyl acetate, and (iv) silyl derivatization to analyze samples with gas chromatography-mass spectrometry (GC-MS) in ion monitoring detection mode (SIM). The results indicated successful detection and quantification of all analyzed compounds without the need for sample dilution. However, our results showed that the method of adding external standards was more suitable for quantifying wine compounds, owing to the matrix effect. Furthermore, this method is promising for quantifying other metabolites present in wines, depending on their extractability with ethyl acetate. In other words, the proposed method may be suitable for profiling (targeted) or fingerprinting (untargeted) strategies to quantify wine metabolites or to classify wines according to the type of winemaking process, grape, or fermentation. Full article
(This article belongs to the Special Issue Application of Chromatographic and Spectroscopic Techniques in Food Adulteration and Traceability)
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12 pages, 1849 KiB  
Article
The Determination of Triacylglycerols and Tocopherols Using UHPLC–CAD/FLD Methods for Assessing the Authenticity of Coffee Beans
by Lama Ismaiel, Benedetta Fanesi, Anastasiya Kuhalskaya, Laura Barp, Sabrina Moret, Deborah Pacetti and Paolo Lucci
Foods 2023, 12(23), 4197; https://doi.org/10.3390/foods12234197 - 21 Nov 2023
Viewed by 942
Abstract
The authenticity of coffee beans was addressed in this study using an analytical method with minimal sample preparation to achieve simple oil extraction and through the implementation of cost-effective equipment. For this purpose, methods using UHPLC with CAD and FLD detectors were applied [...] Read more.
The authenticity of coffee beans was addressed in this study using an analytical method with minimal sample preparation to achieve simple oil extraction and through the implementation of cost-effective equipment. For this purpose, methods using UHPLC with CAD and FLD detectors were applied to detect triglycerides and tocopherols in coffee, respectively. The coffee samples included two main varieties: Arabica from Brazil, Colombia, Ethiopia, and Uganda, as well as the Robusta variety from Cambodia, Guatemala, India, and Vietnam. The samples were either in their green state or subjected to different roasting levels. The used methods successfully distinguished the Arabica and Robusta variants targeted in this study based on their tocopherols and TAG profiles, with the latter being particularly effective for discriminating the origins of the Arabica coffee, while tocopherols excelled at differentiating the origin of the Robusta coffee. TAGs and tocopherols were not affected by the type of roasting, from medium to very dark, suggesting it is possible to distinguish between coffee varieties independently from their degree of roasting. The obtained results hold valuable implications for future research regarding coffee fraud and authenticity. Full article
(This article belongs to the Special Issue Application of Chromatographic and Spectroscopic Techniques in Food Adulteration and Traceability)
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17 pages, 2171 KiB  
Article
Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods
by Chu Chu, Haitong Wang, Xuelu Luo, Peipei Wen, Liangkang Nan, Chao Du, Yikai Fan, Dengying Gao, Dongwei Wang, Zhuo Yang, Guochang Yang, Li Liu, Yongqing Li, Bo Hu, Zunongjiang Abula and Shujun Zhang
Foods 2023, 12(20), 3856; https://doi.org/10.3390/foods12203856 - 21 Oct 2023
Cited by 1 | Viewed by 1116
Abstract
Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water [...] Read more.
Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning—especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)—exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS. Full article
(This article belongs to the Special Issue Application of Chromatographic and Spectroscopic Techniques in Food Adulteration and Traceability)
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15 pages, 1324 KiB  
Article
Seasonal Variation in Raw Milk VOC Profile within Intensive Feeding Systems
by Carmela Zacometti, Alessandra Tata, Andrea Massaro, Giorgia Riuzzi, Marco Bragolusi, Giulio Cozzi, Roberto Piro, Sara Khazzar, Gabriele Gerardi, Flaviana Gottardo and Severino Segato
Foods 2023, 12(9), 1871; https://doi.org/10.3390/foods12091871 - 30 Apr 2023
Cited by 1 | Viewed by 1623
Abstract
The study aimed to assess the seasonal variation in raw milk volatile organic compounds (VOCs) from three indoor feeding systems based on maize silage (n = 31), silages/hay (n = 19) or hay (n = 16). After headspace solid-phase microextraction [...] Read more.
The study aimed to assess the seasonal variation in raw milk volatile organic compounds (VOCs) from three indoor feeding systems based on maize silage (n = 31), silages/hay (n = 19) or hay (n = 16). After headspace solid-phase microextraction (HS-SPME), VOC profiles were determined by gas chromatography (GC). Chemical and VOC (log10 transformations of the peak areas) data were submitted to a two-way ANOVA to assess the feeding system (FS) and season (S) effects; an interactive principal component analysis (iPCA) was also performed. The interaction FS × S was never significant. The FS showed the highest (p < 0.05) protein and casein content for hay-milk samples, while it did not affect any VOCs. Winter milk had higher (p < 0.05) proportions of protein, casein, fat and some carboxylic acids, while summer milk was higher (p < 0.05) in urea and 2-pentanol and methyl aldehydes. The iPCA confirmed a seasonal spatial separation. Carboxylic acids might generate from incomplete esterification in the mammary gland and/or milk lipolytic activity, while aldehydes seemed to be correlated with endogenous lipid or amino acid oxidation and/or feed transfer. The outcomes suggested that VOCs could be an operative support to trace raw milk for further mild processing. Full article
(This article belongs to the Special Issue Application of Chromatographic and Spectroscopic Techniques in Food Adulteration and Traceability)
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10 pages, 2239 KiB  
Article
Dipicolinic Acid-Tb3+/Eu3+ Lanthanide Fluorescence Sensor Array for Rapid and Visual Discrimination of Botanical Origin of Honey
by Xijuan Tu, Yunmin Tao, Jiaxu Chen, Chunping Du, Qian Jin, Yuchang He, Ji Yang, Shaokang Huang and Wenbin Chen
Foods 2022, 11(21), 3388; https://doi.org/10.3390/foods11213388 - 27 Oct 2022
Cited by 2 | Viewed by 1738
Abstract
In the present study, a lanthanide fluorescence sensor array was developed for the discrimination of honey’s botanical origin. Dipicolinic acid (DPA) was used as the antenna ligand for sensitizing the fluorescence of Tb3+ and Eu3+ to prepare the DPA-Tb3+/Eu [...] Read more.
In the present study, a lanthanide fluorescence sensor array was developed for the discrimination of honey’s botanical origin. Dipicolinic acid (DPA) was used as the antenna ligand for sensitizing the fluorescence of Tb3+ and Eu3+ to prepare the DPA-Tb3+/Eu3+ complex. This lanthanide fluorescence sensor showed a cross-reactive response to the major constituents of honey, which led to the result that different classes of honey solution exhibited distinct quenching effects on the fluorescence of the DPA-Tb3+/Eu3+ complex. Furthermore, a fluorescence sensor array composed of ten sensors was constructed by adjusting the pH and the component of the DPA-Tb3+/Eu3+ complex to show multivariate responses towards honey. The visual fluorescence image of the sensor array was recorded by using a smartphone under excitation with portable UV lamp. Results indicated that the pattern of the visual image was related with the botanical origin. After extracting the RGB value of each sensor in 96-well plate, the ratio of R/G was used for principal component analysis (PCA). The results showed that three classes of honey (astragalus, logan, and litchi) were well distinguished. Moreover, the value of principal component 1 (PC1) showed good linearity with the composition of mixing honey and could be used for semi-quantitative analysis. The proposed lanthanide fluorescence sensor array presents a visual and portable method for the discrimination of a honey’s origin without the use of analytical instruments, and might provide a novel and simple strategy for the measurement of food origin. Full article
(This article belongs to the Special Issue Application of Chromatographic and Spectroscopic Techniques in Food Adulteration and Traceability)
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